Abstract
In this work, an effort has been made to identify raga of given piece of Carnatic music. In the proposed method, direct raga classification without the use of note sequence has been performed using pitch as the primary feature. The primitive features that are extracted from the probability density function (pdf) of the pitch contour are used for classification. A feature vector of 36 dimension is obtained by extracting some parameters from the pdf. Since non-sequential features are extracted from the signal, artificial neural network (ANN) is used as a classifier. The database used for validating the system consists of 162 songs from 12 ragas. The average classification accuracy is found to be 89.5 %.
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References
Bello, J.P., Daudet, L., Abdallah, S., Duxbury, C., Davies, M., Sandler, M.B.: A tutorial on onset detection in music signals. IEEE Trans. Speech Audio Process. 13(5), 1035–1047 (2005)
Klapuri, A., Davy, M.: Signal processing methods for music transcription. Springer, New York Inc., Secaucus NJ USA (2006)
Pandey, G., Mishra, C., Ipe, P.: Tansen: A system for automatic raga identification. In: Proceedings of Indian International Conference on Artificial Intelligence, pp. 1350–1363 (2003)
Bhattacharjee, A., Srinivasan, N.: Hindustani raga representation and identification: a transition probability based approach. IJMBC 2(1–2), 66–91 (2011)
Sinith, M., Rajeev, K.: Hidden markov model based recognition of musical pattern in South Indian classical music. In: IEEE International Conference on Signal and Image Processing (2006)
Dighe, P., Agrawal, P., Karnick, H., Thota, S., Raj, B.: Scale independent raga identification using chromogram patterns and swara based features. In: IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 1–4 (2013)
Koduri, G.K., Serra, J., Serra, X.: Characterization of intonation in carnatic music by parametrizing pitch histograms. In: International Society for Music Information Retrieval, pp. 199–204 (2012)
Sridhar, R., Geetha, T.: Swara identification for South Indian classical music. In: 9th International Conference on Information Technology (IEEE) (2006)
Shetty, S., Achary, K.K.: Raga mining of indian music by extracting arohana-avarohana pattern. Int. J. Recent Trends Eng. 1(1), 362–366 (2009)
Ranjani, H.G., Arthi, S., Sreenivas, T.V.: Carnatic music analysis: Shadja, swara identification and raga verification in alapana using stochastic models. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 29–32 (2011)
Chordia, P.: Automatic rag classification using spectrally derived tone profiles. Int. Comput. Music Conf. 2004, 1–4 (2004)
Chordia, P., Rae, A.: Raag recognition using pitch-class and pitch-class dyad distributions. In: Proceedings of International Conference on Music Information Retrieval (2007)
G.Koduri, Gulati, S., Rao, P.: A survey of raaga recognition techniques and improvements to the state-of-the-art. SMC (2011)
Krishnaswamy, A.: On the twelve basic intervals in south Indian classical music. In: 115th AES Convention, New York (2003)
Rabiner, L.R.: On the use of autocorrelation analysis for pitch detection. IEEE Trans. Acoust. Speech Signal Process. 25(1), 24–33 (1977)
Suykens, J.A.K., Vandewalle, J.P.L., Moor, B.L.R.D.: Artificial Neural Networks for Modelling and Control of Non-Linear Systems. Springer, US (1996)
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Suma, S.M., Koolagudi, S.G. (2015). Raga Classification for Carnatic Music. In: Mandal, J., Satapathy, S., Kumar Sanyal, M., Sarkar, P., Mukhopadhyay, A. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 339. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2250-7_86
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DOI: https://doi.org/10.1007/978-81-322-2250-7_86
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